As per the information provided by WHO, most of the people die from the cardiovascular disease. In 2019, almost 32% of the global deaths were due to cardiovascular diseases, out of which 85% were because of heart attacks and strokes. Hence, it is very important to predict the chances and risk of cardiovascular disease event, to prevent any damage in future. Cardiovascular diseases are the disorders of blood vessels supplying blood to heart, brain and different parts of the body. There are different causes of cardiovascular diseases, which can be quantified with the help of different features and with supporting attributes like age of the person, any diseases like diabetes, blood pressure etc., the risk of the cardiovascular disease can be assessed to prevent the further losses. Machine learning approach is very useful in these circumstances, where quantified data values are available in terms of data set. Machine learning techniques can be used to find the risk of cardiovascular disease. Here, we are proposing to use the two machine learning classifiers such as kNN and decision tree. kNN helps us to find the possibility of cardiovascular disease and decision tree helps us to classify the type of the cardiovascular disease with the risk involved. This approach is very useful, as decision tree is one of the most accurate classifiers, which also helps us to identify the specific cardiovascular disease that can be the future event based on feature values. This proposed methodology is justified with proper research gap stating the important of the proposed architecture and implementation results, which gives effective way for assessing the risk of cardiovascular disease.

Network security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

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Network security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumers confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes or identification of normal connection nodes. We expect, everytime whenever there is a connection request, it should be recognized as a type of normal node or malicious node connection request. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as an intrusion and this type of monitoring system is called as Intrusion detection system(IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Suppor Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage & processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

No abstract